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Google’s DeepMind has developed an AI-powered weather forecasting model, GraphCast, that can deliver 10-day predictions in under a minute. According to research by scientists, GraphCast has surpassed the accuracy of traditional weather pattern prediction technologies with a 90% verification rate.
The GraphCast weather prediction program uses the two most recent states of Earth’s weather, including variables from the current time and six hours prior, to predict the state of the weather six hours and up to 10 days ahead.
Google boasts over a million global grid points for hyperlocal data, and GraphCast only requires two pieces of information to make its predictions.
The research claims that this represents a turning point in weather forecasting by enabling cheaper, more accurate, and more accessible predictions that can be tailored to specific applications. This, in turn, can help individuals and industries make better weather-dependent decisions.
The GraphCast model was compared to the European Centre for Medium-Range Weather Forecasts’ High RESolution forecast (HRES), which is considered the gold-standard system. GraphCast was found to be 99.7% more accurate in some cases.
The tool is open-source, enabling anyone to use it, and the ECMWF is already experimenting with it.
Google said that the system can detect extreme weather events before their arrival. During training, GraphCast was able to predict cyclone movement more accurately than HRES. In September, it predicted that Hurricane Lee would reach Nova Scotia nine days before landfall, three days earlier than traditional models.
The model has the ability to predict severe weather events, such as tropical cyclones and extreme temperature waves that occur in different regions. Moreover, the algorithm can be retrained with up-to-date data, which makes scientists believe that the tool will become more accurate in predicting weather pattern changes that align with climate change.
Google is looking at how it can incorporate GraphCast into its products. The National Oceanic and Atmospheric Administration (NOAA) has been working on developing models that will provide more accurate readings on the timing of severe weather events and, importantly, the intensity forecasts for hurricanes.
The GraphCast weather prediction program uses the two most recent states of Earth’s weather, including variables from the current time and six hours prior, to predict the state of the weather six hours and up to 10 days ahead.
Google boasts over a million global grid points for hyperlocal data, and GraphCast only requires two pieces of information to make its predictions.
The research claims that this represents a turning point in weather forecasting by enabling cheaper, more accurate, and more accessible predictions that can be tailored to specific applications. This, in turn, can help individuals and industries make better weather-dependent decisions.
The GraphCast model was compared to the European Centre for Medium-Range Weather Forecasts’ High RESolution forecast (HRES), which is considered the gold-standard system. GraphCast was found to be 99.7% more accurate in some cases.
The tool is open-source, enabling anyone to use it, and the ECMWF is already experimenting with it.
Google said that the system can detect extreme weather events before their arrival. During training, GraphCast was able to predict cyclone movement more accurately than HRES. In September, it predicted that Hurricane Lee would reach Nova Scotia nine days before landfall, three days earlier than traditional models.
The model has the ability to predict severe weather events, such as tropical cyclones and extreme temperature waves that occur in different regions. Moreover, the algorithm can be retrained with up-to-date data, which makes scientists believe that the tool will become more accurate in predicting weather pattern changes that align with climate change.
Google is looking at how it can incorporate GraphCast into its products. The National Oceanic and Atmospheric Administration (NOAA) has been working on developing models that will provide more accurate readings on the timing of severe weather events and, importantly, the intensity forecasts for hurricanes.
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